Federal and State Tax law has become so complex that it is now estimated that each year Americans alone use over 6 billion person hours, and spend nearly 4 billion dollars, in an effort to comply with Federal and State Tax statutes. Given this level of complexity and cost, it is not surprising that more and more taxpayers find it necessary to obtain help, in one form or another, to prepare their taxes. Tax return preparation systems, such as tax return preparation software programs and applications, represent a potentially flexible, highly accessible, and affordable source of tax preparation assistance. However, traditional tax return preparation systems are, by design, fairly generic in nature and often lack the malleability to meet the specific needs of a given user.
For instance, traditional tax return preparation systems often present a fixed, e.g., predetermined and pre-packaged, structure or sequence of questions to all users as part of the tax return preparation interview process. This is largely due to the fact that the traditional tax return preparation system analytics use a sequence of interview questions, and/or other user experiences, that are static features and that are typically hard-coded elements of the tax return preparation system and do not lend themselves to effective or efficient modification. As a result, the user experience, and any analysis associated with the interview process and user experience, is a largely inflexible component of a given version of the tax return preparation system. That is, there is little or no opportunity for any analytics associated with the interview process, and/or user experience, to evolve to meet a changing situation or the particular needs of a given taxpayer, even as more information about the particular taxpayer and their particular circumstances is obtained.
As an example, using traditional tax return preparation systems, the sequence of questions and other user experience elements presented to a user are predetermined based on a generic user model that is, in fact and by design, not accurately representative of any “real-world” user. Consequently, irrelevant, confusing, and impersonal user experiences are presented to any given real-world user. Furthermore, user preferences for user experience content, questions, and/or sequences of questions can change with time because user preferences are regularly swayed and/or altered based on information received from traditional media (e.g., magazines), social media (e.g., Facebook), world events, and the like. It is therefore not surprising that many users, if not all users, of these traditional tax return preparation systems receive, at best, an impersonal, unnecessarily long, confusing, and/or complicated interview process and user experience. Clearly, this is not the type of impression that results in happy and loyal repeat customers.
Even worse is the fact that, in many cases, the hard-coded and static analysis features associated with traditional tax return preparation systems, and the resulting presentation of irrelevant questioning and user experiences, leads potential users of traditional tax return preparation systems, i.e., potential customers, to believe that the tax return preparation system is not applicable to them, and perhaps is unable to meet their specific needs. In other cases, the users simply become frustrated with these irrelevant lines of questioning and other user experience elements. Many of these potential users and customers then simply abandon the process and the tax return preparation systems completely, i.e., never become paying customers. As a result, the potential customers do not become proponents for the tax return preparation systems (e.g., by promoting the product to their friends and family), and may instead become opponents to the tax return preparation systems (e.g., by recommending against the use of the systems). This is an undesirable result for both the potential user of the tax return preparation system and the provider of the tax return preparation system.
Some of the shortcomings associated with traditional software systems, e.g., tax return preparation systems, are a result of insufficient, inadequate, and/or antiquated testing techniques. However, even if service providers want to adjust or customize the user experience flow, it can be very difficult to quickly and efficiently ascertain user preferences for content and determine the effect of various types of content on users. Furthermore, the mere act of taking steps to resolve overly long, impersonal, confusing, and/or complicated user experiences can create additional implementation problems to overcome. In short, any potential attempt at personalizing tax return preparation system and/or other software systems is a very difficult task.
What is needed is a method and system for applying dynamic and adaptive testing techniques (e.g., with the use of an analytics model) to a software system to improve selection of predictive models for personalizing user experiences in the software system, according to various embodiments.